{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import math\n",
    "import numpy as np\n",
    "import random\n",
    "import scipy\n",
    "# 最小二乘求解非线性方程组\n",
    "from scipy.optimize import leastsq,fsolve,root\n",
    "from osgeo import gdal,gdalconst\n",
    "import pandas as pds\n",
    "from scipy import interpolate\n",
    "from multiprocessing import pool\n",
    "from matplotlib import pyplot as plt\n",
    "# 加载自己库\n",
    "from ImageHandle import ImageHandler\n",
    "from sample_process import read_sample_csv,combine_sample_attr,ReprojectImages2,read_tiff,check_sample,split_sample_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输入\n",
    "# a, 工作空间\n",
    "work_path=r\"D:\\Left\\work_space\"\n",
    "# b. 结果工作\n",
    "result_dir_path=r\"D:\\Left\\result\"\n",
    "# c. 是否绘制图像\n",
    "draw_flag=True\n",
    "# 1. 后向散射系数 dB\n",
    "sigma_path=r\"D:\\Left\\left\\BackScatteringProduct\\GF3_MDJ_QPSI_031847_E116.4_N43.9_20220828_L4_HH_L10006708819.tif\"\n",
    "# 2. 局地入射角\n",
    "incident_angle_path=r\"D:\\Left\\left\\BackScatteringProduct\\LocalIncidenceAngle.tif\"\n",
    "# 3. 样本csv地址\n",
    "lai_csv_path=r\"D:\\Left\\LAI样本.csv\"\n",
    "# 4. NDVI影像地址 -- 修正模型\n",
    "NDVI_tiff_path=r\"D:\\Left\\left\\202208NDVI.tif\"\n",
    "# 5. 土壤含水量影像地址 \n",
    "soil_water_tiff_path=r'D:\\Left\\left\\202208.tif'\n",
    "# 6. 土壤含水量样本地址\n",
    "soil_water_csv_path=r\"\"\n",
    "\n",
    "# 7. 选择土壤含水量影像\n",
    "soil_water='tiff' \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\sigma_{soil}=M\\cdot V_{water}+N\n",
    "$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "# 临时变量\n",
    "soil_tiff_resample_path=os.path.join(work_path,\"soil_water.tiff\")   # 与 后向散射系数同样分辨率的 土壤水分影像\n",
    "NDVI_tiff_resample_path=os.path.join(work_path,'NDVI.tiff') # 与 后向散射系数产品同样分辨率的 NDVI影像\n",
    "\n",
    "# 读取数据\n",
    "lai_sample=read_sample_csv(lai_csv_path)# 读取样本数据\n",
    "sigma_tiff=read_tiff(sigma_path)# 读取后向散射系数\n",
    "incident_angle=read_tiff(incident_angle_path)   # 读取局地入射角\n",
    "\n",
    "# 对于土壤水分、NDVI做重采样\n",
    "ReprojectImages2(soil_water_tiff_path,sigma_path,soil_tiff_resample_path,resampleAlg=gdalconst.GRA_Bilinear) \n",
    "ReprojectImages2(NDVI_tiff_path,sigma_path,NDVI_tiff_resample_path,resampleAlg=gdalconst.GRA_Bilinear) \n",
    "soil_water_tiff=read_tiff(soil_tiff_resample_path) # 读取土壤含水量影像\n",
    "NDVI_tiff=read_tiff(NDVI_tiff_resample_path) # 引入NDVI\n",
    "soil_water_tiff['data']=soil_water_tiff['data']/100.0   # 转换为百分比\n",
    "incident_angle['data']=incident_angle['data']*np.pi/180.0   # 转换为弧度值\n",
    "sigma_tiff['data']=np.power(10,(sigma_tiff['data']/10)) # 转换为线性值\n",
    "\n",
    "# float32 转 float64\n",
    "soil_water_tiff['data']=soil_water_tiff['data'].astype(np.float64)\n",
    "incident_angle['data']=incident_angle['data'].astype(np.float64)\n",
    "sigma_tiff['data']=sigma_tiff['data'].astype(np.float64)\n",
    "\n",
    "# 将土壤水分与lai样本之间进行关联\n",
    "lai_water_sample=[] # ['日期', '样方编号', '经度', '纬度', 'LAI','土壤含水量']\n",
    "if soil_water=='tiff': \n",
    "    lai_water_sample=combine_sample_attr(lai_sample,soil_water_tiff)\n",
    "    pass\n",
    "else: # 这个暂时没有考虑\n",
    "    pass\n",
    "\n",
    "# 将入射角、后向散射系数与lai样本之间进行关联\n",
    "lai_water_inc_list=combine_sample_attr(lai_water_sample,incident_angle) # ['日期', '样方编号', '经度', '纬度', 'LAI','土壤含水量','入射角']\n",
    "lai_waiter_inc_sigma_list=combine_sample_attr(lai_water_inc_list,sigma_tiff)# ['日期', '样方编号', '经度', '纬度', 'LAI','土壤含水量','入射角','后向散射系数']\n",
    "# ['日期', '样方编号', '经度', '纬度', 'LAI','土壤含水量','入射角','后向散射系数','NDVI']    \n",
    "lai_waiter_inc_sigma_NDVI_list=combine_sample_attr(lai_waiter_inc_sigma_list,NDVI_tiff)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ['日期', '样方编号', '经度', '纬度', 'LAI','土壤含水量','入射角','后向散射系数','NDVI'] \n",
    "lai_waiter_inc_sigma_NDVI_list=check_sample(lai_waiter_inc_sigma_NDVI_list) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43, 9)\n"
     ]
    }
   ],
   "source": [
    "# ['日期' 0, '样方编号' 1, '经度' 2 , '纬度' 3, 'LAI' 4 ,'土壤含水量' 5,'入射角' 6,'后向散射系数' 7 ,'NDVI' 8] \n",
    "lai_sample=np.array(lai_waiter_inc_sigma_NDVI_list)                 \n",
    "sample_lai=lai_sample[:,4].astype(np.float64)\n",
    "sample_soil=lai_sample[:,5].astype(np.float64)\n",
    "sample_inc=lai_sample[:,6].astype(np.float64)\n",
    "sample_sigma=lai_sample[:,7].astype(np.float64)\n",
    "sample_NDVI=lai_sample[:,8].astype(np.float64)\n",
    "print(lai_sample.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lai-soil 0.015787994421483577\n",
      "lai-inc -0.20905135876570902\n",
      "lai-sigma 0.011227943667788719\n",
      "lai-NDVI 0.3039670754532818\n",
      "soil-sigma 0.3178306866806234\n"
     ]
    }
   ],
   "source": [
    "# 计算相关性\n",
    "print(\"lai-soil\",np.corrcoef(sample_lai,sample_soil)[0,1])\n",
    "print(\"lai-inc\",np.corrcoef(sample_lai,sample_inc)[0,1])\n",
    "print(\"lai-sigma\",np.corrcoef(sample_lai,sample_sigma)[0,1])\n",
    "print(\"lai-NDVI\",np.corrcoef(sample_lai,sample_NDVI)[0,1])\n",
    "print(\"soil-sigma\",np.corrcoef(sample_soil,sample_sigma)[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(sample_lai,label='lai')\n",
    "plt.plot(sample_soil,label='soil')\n",
    "#plt.plot(sample_inc,label='inc')\n",
    "plt.plot(sample_sigma,label='sigma')\n",
    "#plt.plot(sample_NDVI,label='NDVI')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 ('work')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "80a0d521a065b96b42e1a8d137ebbef2c33292df86935ae89cbf5670939ae5c3"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
